



import os

import numpy
import onnx

def  lowerCamelCase_(lowerCamelCase_     , lowerCamelCase_     )			->							Dict:
						UpperCAmelCase								=  a.name
						UpperCAmelCase								=  b.name

						UpperCAmelCase								=  ""
						UpperCAmelCase								=  ""

						UpperCAmelCase								=  a == b

						UpperCAmelCase								=  name_a
						UpperCAmelCase								=  name_b

						return res

def  lowerCamelCase_(lowerCamelCase_     , lowerCamelCase_     , lowerCamelCase_     )			->							Tuple:






						for i, input_name in enumerate(node_proto.input     ):
												if input_name == name:
																		node_proto.input.insert(UpperCamelCase_     , UpperCamelCase_     )
																		node_proto.input.pop(i + 1     )

						if node_proto.op_type == "If":
												_graph_replace_input_with(node_proto.attribute[0].g     , UpperCamelCase_     , UpperCamelCase_     )
												_graph_replace_input_with(node_proto.attribute[1].g     , UpperCamelCase_     , UpperCamelCase_     )
						if node_proto.op_type == "Loop":
												_graph_replace_input_with(node_proto.attribute[0].g     , UpperCamelCase_     , UpperCamelCase_     )

def  lowerCamelCase_(lowerCamelCase_     , lowerCamelCase_     , lowerCamelCase_     )			->							str:






						for n in graph_proto.node:
												_node_replace_input_with(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     )

def  lowerCamelCase_(lowerCamelCase_     , lowerCamelCase_     , lowerCamelCase_     )			->							Union[str, Any]:
						UpperCAmelCase								=  list(model.graph.initializer     )
						UpperCAmelCase								=  list(model_without_ext.graph.initializer     )
						for i, ref_i in ind_to_replace:
												assert inits_with_data[i].name == inits[i].name
												assert inits_with_data[ref_i].name == inits[ref_i].name
												assert i > ref_i

												UpperCAmelCase								=  inits[i].name
												UpperCAmelCase								=  inits[ref_i].name

												model_without_ext.graph.initializer.remove(inits[i]     )

												# for n in model.graph.node:
												_graph_replace_input_with(model_without_ext.graph     , UpperCamelCase_     , UpperCamelCase_     )





def  lowerCamelCase_(lowerCamelCase_     )			->							Union[str, Any]:
						UpperCAmelCase								=  os.path.dirname(UpperCamelCase_     )
						UpperCAmelCase								=  os.path.basename(UpperCamelCase_     )

						UpperCAmelCase								=  onnx.load(os.path.join(UpperCamelCase_     , UpperCamelCase_     )     )

						UpperCAmelCase								=  list(model.graph.initializer     )

						UpperCAmelCase								=  set()
						UpperCAmelCase								=  {}
						UpperCAmelCase								=  []

						UpperCAmelCase								=  0

						for i in range(len(UpperCamelCase_     )     ):
												if i in dup_set:
																		continue

												for j in range(i + 1     , len(UpperCamelCase_     )     ):
																		if j in dup_set:
																								continue
																		if _is_equal_tensor_proto(inits[i]     , inits[j]     ):
																								dup_set.add(UpperCamelCase_     )
																								dup_set.add(UpperCamelCase_     )

																								UpperCAmelCase								=  inits[j].data_type
																								UpperCAmelCase								=  numpy.prod(inits[j].dims     )
																								if dtype == 1:
																														mem_size *= 4
																								elif dtype == 6:
																														mem_size *= 4
																								elif dtype == 7 or dtype == 11:
																														mem_size *= 8
																								else:
																														print("unexpected data type: "     , UpperCamelCase_     )
																								total_reduced_size += mem_size

																								UpperCAmelCase								=  inits[i].name
																								UpperCAmelCase								=  inits[j].name

																								if name_i in dup_map:
																														dup_map[name_i].append(UpperCamelCase_     )
																								else:
																														UpperCAmelCase								=  [name_j]
																								ind_to_replace.append((j, i)     )

						print("total reduced size: "     , total_reduced_size / 1_024 / 1_024 / 1_024     , "GB"     )

						UpperCAmelCase								=  sorted(UpperCamelCase_     )
						_remove_dup_initializers_from_model(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     )

						UpperCAmelCase								=  "optimized_" + model_file_name
						UpperCAmelCase								=  os.path.join(UpperCamelCase_     , UpperCamelCase_     )
						onnx.save(UpperCamelCase_     , UpperCamelCase_     )

						return new_model







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE     :					Optional[Any]      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					int      =       {
    "facebook/deit-base-distilled-patch16-224": (
        "https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						'deit'
    def __init__(			self     ,   __snake_case=7_6_8     ,   __snake_case=1_2     ,   __snake_case=1_2     ,   __snake_case=3_0_7_2     ,   __snake_case="gelu"     ,   __snake_case=0.0     ,   __snake_case=0.0     ,   __snake_case=0.02     ,   __snake_case=1E-12     ,   __snake_case=2_2_4     ,   __snake_case=1_6     ,   __snake_case=3     ,   __snake_case=True     ,   __snake_case=1_6     ,   **__snake_case     ,   ):
         super().__init__(**__snake_case       )

         snake_case											=							hidden_size
         snake_case											=							num_hidden_layers
         snake_case											=							num_attention_heads
         snake_case											=							intermediate_size
         snake_case											=							hidden_act
         snake_case											=							hidden_dropout_prob
         snake_case											=							attention_probs_dropout_prob
         snake_case											=							initializer_range
         snake_case											=							layer_norm_eps
         snake_case											=							image_size
         snake_case											=							patch_size
         snake_case											=							num_channels
         snake_case											=							qkv_bias
         snake_case											=							encoder_stride






class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						version.parse('1.11'      )
    @property
    def 						a_     (			self       ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]       )




    @property
    def 						a_     (			self       ):
         return 1E-4




